The New Jobs for Humans in the AI Era

When OpenAI unleashed its humanlike ChatGPT software on the world last year, one thing was clear: These AI systems are coming for our jobs. But don’t write off the humans just yet. More than a century ago, the advent of the automobile was bad news for stable hands, but good for mechanics. And AI is already creating new opportunities. Here are a few of them.

In-House Large Language Model Developer

Large language models such as OpenAI’s GPT and Google’s LaMDA are trained on massive amounts of data scraped from the internet to recognize, generate and predict language in sequences. For the finance industry, that makes them a bit like new college graduates: Not much use without more specialized instruction.

In-house developers will change that by introducing the models to new word patterns that will equip them to better carry out functions such as summarizing a company’s 10-K annual report filing or guiding a client through a loan-application process.

“What we currently are heading toward is some small number of companies developing these humongous models and then customers—financial institutions—taking those models and then training them better for their own purposes in-house,” says Eric Ghysels, a professor of economics and finance at the University of North Carolina, Chapel Hill.

In-house developers will design the curriculum for this training by choosing new and often proprietary data to run through the models. They will habituate models to legalese and to the financial meaning of words such as “interest” and “derivative” by querying them and responding with constructive feedback on their answers. Finally, they will deliver the models in a user-friendly form to employees and clients.

The challenge for financial institutions, says Ghysels, will be finding people qualified to do all this.

Reskiller

As AI becomes capable of taking on more work that is now done by humans, people will need to more aggressively upgrade their skills to stay productive and employable. “Reskillers,” a new type of teacher, will help people stay one step ahead of the machines.

As AI evolves, companies will put growing value on specialists who can guide such critical human development. “Teachers had it bad under the industrial revolution. Look at what they are paid,” says Stephen Messer, co-founder and chairperson of Collective[i], which has developed a foundation model that produces insights around revenue forecasting and growth. “Now, I think teachers are about to go through a revolution because of AI.”

Reskillers will need to understand the talents that organizations require as technology marches ahead. “This puts an onus on employees and companies to stay relevant,” says Keith Peiris, co-founder and chief executive of Tome, a startup with a generative AI-native storytelling and presentation platform. “In the ‘old world,’ pre generative AI, maybe you needed 100 people to build a company…With AI, maybe you could build that company with 30 people.”

New career-development arcs are already taking shape because of generative AI, according to Peiris, who is trained in nanotechnology engineering and is a veteran of Facebook and Instagram. “Sales professionals are learning web development and copywriting. Marketers are becoming more steeped in graphic design,” he says. “HR professionals are taking on “legal work” and becoming paralegals by using AI legal tools.”

AI Psychotherapist

Financial firms relying on AI for prediction and decision-making will need people to divine the drivers of a model’s thinking.

Unlike conventional software, the logic behind the output of applications such as OpenAI’s ChatGPT is typically opaque. That may be fine when they’re used to generate things like recipes and poems, says Dinesh Nirmal, senior vice president, products, IBM Software, but not if they’re relied upon for things such as assigning credit scores, optimizing investment portfolios and predicting liquidity balances.

Business or enterprise AI, which serves firms and organizations, is all about “explainability,” says Nirmal.

Customers will want to know why their loan application was rejected. Bank regulators will require some decisions to be explained.

AI psychotherapists will evaluate a model’s upbringing, by scrutinizing its training data for errors and sources of bias. They may put AI models on the couch, by probing them with test questions. Companies such as IBM, Google and Microsoft are racing to release new tools that quantify and chart an AI’s thought processes, but like Rorschach tests they require people to interpret their outputs.

Understanding an AI’s reasoning will only be half the job, says Alexey Surkov, partner and global head of model risk management at Deloitte & Touche. The other half will be signing off on a model’s mental fitness for the task at hand. “No matter how sophisticated the models and systems get,” says Surkov, “we as humans are ultimately responsible for the outcomes of the use of those systems.”

“Psychotherapist” might be a stretch for a job title in some financial firms. Surkov suggests AI Risk Manager or Controller as alternatives.

Prompt Engineer

How do you program an AI system like ChatGPT that can converse with you, much like a human? You talk to it. Or, more precisely, you hire a prompt engineer to do this. Prompt engineering is an emerging class of job that is nestled somewhere between programming and management. Instead of using complicated computer programming languages like Python or Java, prompt engineers will spell out their instructions to AI systems in plain English, creating new ways of harnessing the power of the underlying AI systems.

This is what legal software maker Casetext’s new class of engineers do with its AI-based legal assistant called CoCounsel. Jake Heller, the company’s chief executive, says he’s hiring prompt engineers to build out CoCounsel’s abilities by instructing the AI chatbot, in maybe a thousand words or so, how to do various legal tasks. The language the prompt engineers use is precise and to the point, explaining how to review documents, summarize research or review and edit a contract. For example, a prompt engineer may start to outline instructions for a CoCounsel memo by indicating the level of expertise needed: “Your goal is that the memo will display the level of perception, nuance, and attention to detail one would expect from a federal appellate judge drafting a legal opinion.”

The best prompt engineers are people who can give very clear instructions, but who also understand the principles of coding, Heller says. In other words, they’re often great technical managers. Except with prompt engineers, it’s not an employee that they’re managing, he says. “It’s an AI.”

Write to Robert McMillan at robert.mcmillan@wsj.com, Bob Henderson at bob.henderson@wsj.com and Steven Rosenbush at steven.rosenbush@wsj.com