Tejas Dessai, AVP, Research Analyst at Global X ETFs, explains that we are at a pivotal moment in robotics. Automation has the potential to disrupt various industries, particularly in manufacturing, with benefits including enhanced safety and increased efficiency. He also discusses the convergence of software and hardware advancements, marking a “golden moment” for AI.
Tejas Dessai is AVP, Research Analyst at Global X ETFs. In that role, Dessai leads research for Global X’s ETFs focused on disruptive technology, including artificial intelligence (AI), cloud, digital commerce, gaming, financial technology, social media and cybersecurity.
Before joining Global X, Dessai was Senior Research Associate at investment research platform SSR, where he covered market leaders and steered qualitative and quantitative analysis on emerging trends.
He graduated with a bachelor’s degree in technology, electronics and communication engineering from the National Institute of Technology, Goa, and with a master’s degree in industrial and systems engineering from North Carolina State University.
“This is an interesting moment in history for robotics, broadly,” says Dessai.
“On the one hand you have a tonne of technological progress that makes automation possible. We’ve been seeing this digitalisation of the economy play out for the past three or four decades. But at the same time, the economics are aligning for the increased use of automation, especially in developed markets: we have an ongoing labour shortage and we are investing significantly to bring manufacturing back to the western hemisphere.”
This has created an “inflection point” for robotics.
One prominent use for automation is in factories, and manufacturing more broadly. The value lies in saving individuals from physically demanding or risky tasks on factory floors.
There is also a growing need for improved efficiency and product quality, as well as increased production rates.
He points to Tesla [TSLA] — a company with “significantly higher production rate and fleet utilisation rate per employee relative to some of the other automakers” — as a particularly successful use case.
Looking ahead, the trajectory appears to lead to a future where human workers oversee robots managing factory operations. “In that situation, the promise and opportunity are pretty big. It’s a great time to be within the space,” he says.
If having to choose between one employee on the floor fitting and building things, or one employee managing 10 robots that can do the same work, “the latter is a lot more valuable. It’s really our intelligence, our ability to make decisions and process information, that unlocks value.”
Driving Down Costs and Improving Quality
Robotics, over time, has the potential to reduce the cost of goods, especially those manufactured in factories, by virtue of being more cost-effective than human labour. This efficiency can lead to a reduction in overall production costs, provided there is healthy competition in the market.
“Labour economics is one part of the equation here,” Dessai says. “But I also think the quality of products could go up significantly.”
Unlike humans, machines can operate 24/7, increasing utilisation on longer production lines. Meanwhile, mechanisation could lead to reduced error rates, particularly for intricate or large-scale manufacturing.
“It positions us to make the most of this new wave of globalisation,” he says.
As with any technology, risks could emerge. A top concern is job displacement.
“Currently, a large number of people are working in manufacturing; it’s the primary source of earnings for a lot of folks around the world. How do we make sure that the training of those people actually happens so that they can move into a position or role where they work alongside robotic systems?”
There are also ethical considerations. “When we get to the point of complete automation — and I don’t think that it’s very far away, where robots run factories all by themselves — how will we decide whether the quality of a product is good enough?” he asks, pointing to quality control within food processing.
There are also security concerns. “Automation is digitalised and, as a result of that, these systems can be vulnerable and can be hacked.”
He pointed to the June 2021 cyberattack on JBS [JBSAY], the world’s largest meat processor, which forced the shutdown of nine beef plants and disrupted operations at others. “JBS was basically held hostage for a very long time — something similar could disrupt the entire supply chain, or take prices up and cause all sorts of problems,” he says.
In recent years, security concerns have been fuelled by the advancement of software technologies. “Robotics is the combination of AI, which is the brain, and hardware components, which I like to say are the hands of the system. The brain and the hands have to always be in sync for optimal results.”
However, the software aspect of robotics has traditionally been a challenge. AI development faced obstacles due to its limited scalability until a few years ago, which hindered its deployment on machines operating in resource-constrained environments.
“AI is currently at a very interesting point with large language models. We’ve clearly unlocked a new frontier of innovation in terms of capability. And the best part about these models is that they’re inherently distributable,” Dessai explains.
Due to the falling cost of hardware and the rising accessibility of AI, “it’s becoming increasingly possible economically to deploy AI systems in the real world”.
AI: Intelligence as a Commodity
“This is the golden moment for AI,” Dessai says. The emergence of technologies such as ChatGPT, which has been publicly available for less than 14 months, has breathed new life into AI projects that were previously shelved.
“If you look at its core, what we’re doing with AI is, to a certain degree, commoditising intelligence.” Looking at knowledge work broadly, a substantial portion can be automated.
In the financial services sector, for example, a considerable amount of time is dedicated to producing research reports. AI systems can efficiently summarise information and present key insights, massively streamlining the process.
“If you look at its core, what we’re doing with AI is, to a certain degree, commoditising intelligence.”
Similar applications extend to the tech sector, with AI potentially automating code generation and reducing redundant software writing. In healthcare, meanwhile, AI can contribute to tasks like maintaining patient records, sending messages and generating comprehensive diagnosis reports, ultimately improving efficiency and saving valuable time for healthcare professionals.
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