Forces of STEM The ups and downs of an AI augmented labour force

Ben Bschor:

One problem that in particular STEM industries face, is labour shortages, actually. There is basically not enough people around to have the right skills to fill all the roles that are out there and available. What does AI mean for these labour shortages? Could that be addressed? Could AI jump in there and replace workers or at least help them so that one worker can do more and maybe it's easier to fill those roles?

Bhargav Srinivasa Desikan:

From a research evidence perspective, it showed that especially for programming tasks-now programming is of course one kind of STEM job, but it's a good way to give an example of how this works-it's quite a structured task. It's quite a structured task and can often have predictable outcomes, in terms of ins and outs. So, in this particular situation, large language models end up doing quite well. There's also quite a few code-specific large language models. In a study that was done, it basically showed that if you are an entry level programmer, using a large language model to help you to assist in coding, helped you get to nearly the same proficiency as a semi-expert or expert programmer. Now, if you're already an expert programmer, then ChatGPT also still gives you a small boost, but the percentage of that boost is far less than if you were a beginner level programmer.

So this basically means that you suddenly have a lot more capacity and capability. It also means that maybe in the past, when you needed to do a full university undergraduate course at the least, and then have a significant grounding, you could maybe now potentially get away with doing a coding camp or a crash course, for example. So in that case, yeah, it absolutely can. Now, with this comes certain caveats like, you would still want some expert in the room to be able to do a quality analysis. You would still want very, very thorough tests to be written. You wouldn't want to ever only trust a model to do your job for you. You would really want a human in the loop. And that's obvious in a lot of different contexts, especially in critical contexts. But certainly, I think in fact, that could be one way where I'm quite optimistic actually about how generative models can help, because you could maybe sense in our report, we're quite cautious, in general, about how these tools should be used because for example, in healthcare, education, these are crucial societal functions. But in the context of STEM jobs, in the context of say, data analysis, programming, can be quite useful.

In my job as well. It helps me a lot and I benefit a lot in terms of my productivity. If my firm might need to hire two computer scientists right now, they can get away with just me. So, in this case, I think there is a very direct labour shortage solution. We still need more people who are able to understand and use these tools, and it won't completely automate and come in the way. Basically beginners can now work as a semi-expert, or of course, all the caveats, and ifs and buts that come with that.

Ben Bschor:

In a way, it's not only knowing how to use it exactly, but it's about understanding in what areas AI might be able to help me. If I can add from my own perspective, I just started last week playing around with using AI to analyse spreadsheets and it's for me a steep learning curve and extremely interesting.

Final question, Bhargav. How can companies support the workforce during this period of change? Is there something specific they should do?

Bhargav Srinivasa Desikan:

Yeah. At a time when there is a race, in a sense, for any firm that might be, or even around, somehow involved in, say, data in their pipelines or might want to use it as a customer service chatbot or whatever a firm might want to use it for, I would recommend pausing and thinking if 100%, if AI is necessary for that use case.

It would be very important to keep a human in the loop and to help empower your workers in knowing what the capabilities are, having a clear policy on how to use these tools, investment in training to make sure that workers can get the most out of these tools. So, I would say listen to your workers. Quite often, they know what helps them.

We're also at IPPR discussing with the Trade Union Congress on how unions can also work together. Because for society and for firms, to get the most out of AI, workers need to feel empowered and comfortable. If they are feeling like they might be replaced or if they're under surveillance, it's not going to work for anybody. So I just want to put that as a very crucial thing: Firms must work with everybody who might be using these tools in a way that makes sense for both the managers or the decision makers as well as the workers.

And then of course, it really depends on your sector. See for your sector how is AI being used? Be able to see if it's just a snake oil, if they're just selling you something to just use a product. Be sure to be critical and grounded. It's really important to make sure to not just use these tools because everybody else is using them, but to see what a potential use case is. See in a way where it's fairly rolled out to your workers. Make sure that they're educated well on it. Make sure that if there's a productivity increase, there's wage increases as well. So, all of these need to happen to make sure that this is being used in a way in society that really benefits everybody.

Ben Bschor:

This is a great conclusion Bhargav. Thank you so much for joining us today on our Forces of STEM audio feature. Thank you so much.

Bhargav Srinivasa Desikan:

Thank you so much, Ben.

VO: This interview is part of Forces of STEM, a research campaign produced by SThree in partnership with FT Longitude.

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