AI's Productivity Potential: A Bright Future Ahead?

An article we liked from Thought Leader Azeem Azhar:

AI’s productivity paradox: how it might unfold more slowly than we think

From steam engines to GPTs: the long road to productivity

I want to play a game of counterfactuals. If artificial intelligence disappoints as a technology, why might that be?

Historically, technologies like the steam engine and personal computers boosted productivity, raised GDP and reshaped societies—transforming where and how people live, work and even what they value. I believe AI is such a technology and it has the potential to deliver those benefits—and faster than previous technologies.

But, of course, I always like to challenge my own thinking.

So, today I want to explore a scenario where AI disappoints by failing to deliver significant productivity improvements within a short timeframe—say, five years. Through this lens, I’ll examine the challenges that could slow down AI’s impact and think about the implications of a less revolutionary trajectory.

AI as a GPT

Before we discuss potential disappointing scenarios, I want to first lay out the reasons that AI could be a general-purpose technology.

First of all, AI’s potential as a GPT lies in its versatility. GPTs are broadly applicable across sectors, improve rapidly, spur complementary innovations and fundamentally reduce the cost of critical economic activities. Unlike specialized technologies, AI can tackle a vast array of tasks—from summarising emails to designing board games, from writing code to finding new molecules.

Its rapid progress is impressive: large language models have evolved from producing short text fragments to tackling math problems at the level of the top 0.1% of high school students.

AI also fosters complementary innovations. Downstream, we are seeing huge innovations in hardware; performance per dollar of Nvidia GPUs has been improving by around 30% per year. Consider ASICS like Cerebras and Groq or the resurgence of interest in reversible computing.

Focus should go beyond the chips... Just look at what is happening in bandwidth. We have moved networks from 10 megabit ethernet in the 1990s to 1.8 terabit NVLink today – 180 thousand times the speed. And don’t forget innovations in cooling — such as immersion cooling or the use of microfluidics to cool chips.

Upstream we’re seeing wild experimentation. In AI-based middleware we have LangGraph and Wordware both helping developers create sophisticated AI agents. Apps like Cursor help coders work faster, Granola simplifies meeting notes and Elicit speeds up literature reviews for researchers.

AI promises to lower the cost of intelligence — a core input in modern economies. When agents can do a task, they do so at one-thirtieth of the cost of a human.

This combination of adaptability, rapid advancement and cost reduction positions AI to join the ranks of historic general-purpose technologies. But AI is being touted as much more than that – and the expectations of the market are resting on it. So, what would have to happen for it to disappoint?

Framing the criteria for “disappointment”

For AI to succeed as a GPT, it must drive a noticeable increase in productivity growth. History shows that this can take time and follows an S-curved trajectory. In the short term, the impacts of a GPT are marginal across an economy, although individual firms can do well. In the medium term, as the technology becomes more widespread, firms are able to retool around the technology and a plethora of upstream and downstream businesses emerge. This all drives growth. In the long term, a technology’s contribution to productivity growth flatlines as it becomes normalised.

In other words, it’s the medium term which matters, the point at which a technology is far enough dispersed into firms in an economy to have a systematic impact on productivity. In the case of electricity and the PC, America start to see GDP growth increase starting in the 1920s. The technology had been commercialised for 30 years by that point. But it started having economy wide impacts when it about a quarter of homes were electrified.

Economists are rather split about what AI might mean to us economically. There’s a wild range of estimates looking at labour productivity growth alone.

What do those numbers mean in terms of GDP growth? Goldman Sachs, for instance, estimates a 1.5% increase in labour productivity over 10 years for 2023. To make sense of this, let’s turn it into real dollars. US GDP was $27.7 trillion in 2023. This could mean that by the end of the tenth year, US GDP would be between $6 and 7 trillion dollars higher than it otherwise would have been, yielding a cumulative $25-30 trillion dollar increase over the decade.1

For this thought experiment, I’ll define “disappointment” as AI failing to boost labour productivity by 2% by the year five, starting the clock in January 2023. I’m choosing 2% as it’s above most estimates, January 2023 as it’s roughly the ChatGPT starting clock; and five years because many of us, myself included, have argued that AI should get to its point of impact faster than the PC or electricity, which took 15 years and 30 years respectively.

My main line of thinking is that productivity impacts follow the deployment of a technology across the economy and AI can get deployed much faster than the railway: we’re richer and much of the underlying infrastructure, internet and computers, is in place. In addition, the organizational dimensions of the modern economy favor fast adoption. Firms have reorganized themselves dozens of times in the past decades and many of you will have lived through transformation after transformation. This might suggest firms have the capacity and managerial expertise to...

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Thanks for this article excerpt and its graphics to Azeem Azhar.

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