Kailash Nad, CTO of Indian fintech firm Zeroda, recently expressed concern about rapid advances in artificial intelligence (AI). In a blog post, Nadh revealed that Zerodha created his AI policy so that employees would not lose their jobs if AI implementations made their roles unnecessary. The policy includes efforts to improve the skills and provide new opportunities for employees whose jobs are affected by AI.
While Nadh’s blog post was specific to Zerodha, his concerns concern millions of organizations around the world. He noted that his AI advances over the past few months have been particularly significant. The speed at which AI can generate code and integrate it into existing systems is unprecedented. Nad suggests this is a tipping point where AI’s impact on jobs and the economy could be greater than ever before.
Concerns are not only about the jobs lost, but also about the nature of the jobs that will be replaced. Are they equally high-income and safe? Will we need new skills that everyone can access? Nad’s approach at Zeroda is a step in the right direction, but not a complete solution. The reality is that AI’s impact on jobs and the economy is complex and multifaceted.
Moreover, the impact of AI on the economy may be uneven. Some sectors may be affected more than others, and some regions may be affected more than others. For example, jobs in manufacturing and retail may be more susceptible to automation than jobs in healthcare and education. Regions that are highly dependent on specific industries may be more vulnerable to job losses.
The potential impact of AI on jobs and the economy isn’t just a concern for businesses and employees. Governments also have a role to play. Nad is skeptical that governments can effectively regulate AI. He points to the failure of governments to address climate change as evidence that self-regulation may be the only solution.
“The fact that such a policy had to be formulated marks a tipping point, the implications of which I still do not understand. Blockchain, serverless, Web3, big data, or previous AI / ML technology didn’t deliver this, but in the last few months we’ve finally made tangible progress: integration took only 30 minutes, during which time code was generated to integrate itself It feels different this time,” he said in a blog post.
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