The dawn of the much-awaited/much-dreaded Age of Artificial Intelligence (AI) is now undeniable, nearly six months after ChatGPT was launched.

Decision-makers around the world are perceptibly still struggling to understand the real implications of AI, as well as the trajectory of AI over the next few years. As always with the arrival of big new technologies, the doomsayers and enthusiasts are battling it out on social media and in blogs and articles. It remains hard to claim certainty about any prediction.

After all, no one confidently predicted the stunning arrival of Generative AI based on Large Language Models (LLMs).

That said, those who forge ahead on the middle path take a view that while AI might need regulation to curb its possible adverse effects, we must grasp the optimism of its promises.

One of the standout promises is the potential of AI to emphatically turn around long-stagnating productivity levels around the developed world. (Known widely as the ‘Productivity Paradox,’ because the information technology revolution was expected to make productivity soar, but overall productivity has been nearly flat in much of the Western world and Japan for decades).

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The rate of productivity growth is the most robust metric of long-term wealth creation.

For the first time in history, we now have a technology that can work alongside humans as an easy-to-use accelerant in various domains of expertise, and even across several domains. According to a recent paper published by OpenAI researchers and the University of Pennsylvania, up to 8 in 10 workers in the United States will be affected by the introduction of LLMs – in positive ways as well as negative. This is stunning prediction.

But there are key questions behind these predictions:

• What sort of regulation are major governments likely to impose on AI? (This may in the end be determined by growing competition between major powers).
• What new barriers to adoption of AI might emerge? (For example, could industrial action stymie the adoption of AI in some sectors?)
• Will the productivity boost of AI across various sectors be similar, or would only some sectors benefit?
• Are predictions about the pace of AI development reliable?

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A Stanford and MIT study has found that even the current Generative AI is able to boost worker productivity by 14%. This will only get much better over the months and years.

Goldman Sachs Research suggested last month that AI adoption could ‘drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period’ – but added this caveat: ‘Despite significant uncertainty around the potential for generative AI.’ Leaving aside the understandable hedging, the figures predicted in the paper are impressive.

A group at the Brookings Institute reminded sceptics earlier this month that productivity growth compounds over time. So, the smallish-looking lift in productivity growth forecast by Goldman Sachs would result in a 5% increase in the size of the economy, and the compounding wouldn’t simply stop.

Especially because AI development is not expected to grind to a halt. On the contrary, celebrated economist Tyler Cowen has asserted that ‘AI is improving faster than most humans realize.’

Get ready for an exciting ride into the future.

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