Azeem Azhar

speaker

7,268 appearances 34 recordings 2 series first heard Apr 2025 last heard 25 Mar

Azeem Azhar’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 18 in all, peaking in Jan 2026 with 4.

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Azeem Azhar's Exponential View · Why the AI productivity gains haven’t arrived - yet · 21 Nov 2025 podcast

And that is where productivity comes in.

No productivity, no point, no sales, no revenues.

Well, the markets would be right to turn red.

So let's dig into what we can now see from some of the data around productivity three years into this ChatGPT moment.

Well, there's an increasing body of evidence that shows that inside firms, inside teams, with individual workers, AI is making people more productive.

Micron, which is a semiconductor company, it makes the high bandwidth memory as it happens is what you need in these AI data centers, reports 30 to 40% productivity gains when their employees use generative AI for code generation and other internal uses.

So that's a really, really significant number coming from a company that's not one of the big tech firms.

Coinbase, the crypto business, says that AI-generated code is on track to surpass human output.

I think it was 92% of their technical staff using those code generation tools every day.

Now, of course, Coinbase is a relatively young, dynamic company, so it's natural that it would be an early adopter.

So these early adopters are, of course, demonstrating some concrete gains.

But I read something in the Wall Street Journal this week

which was really quite surprising.

It looked at BNY, which is a bank in the US, and Walmart, and came up with a couple of quite strong and surprising examples of how they were using AI and getting some kinds of results.

So in the case of BNY, which used to be called Bank of New York, I think, years ago, that's how I remember it,

They've deployed a hundred digital employees who have their own login credentials and can go in and get work done using the internal systems that human employees are using.

With their own login credentials, they can access the systems they need.

BNY claim this is actually giving them bottom line impacts through growing the capacity to do work.

One example they talk about is a digital engineer that can scan code for vulnerabilities and implement fixes as they get found.

So that's quite an interesting example because, of course, on the one hand, you are just saving the human dollars of doing that.

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