Workers Upgrade Their Skills Amid AI Jobs Boom

 

"Tech workers are feverishly retooling their skill sets for a time when every company suddenly wants to be an artificial-intelligence company -- and every worker feels the need for AI chops.

To try to make that happen, workers are attempting to bridge the gap between what they know and what they need to know, adding skills and knowledge to pivot into this game-changing technology. Tech companies, meanwhile, are refashioning themselves as AI companies and trying to remold their workforces to be more AI proficient.

"I've been leading with an AI-tailored resume for the last two to three months," says Asif Dhanani, 31 years old, of Irvine, Calif., who was laid off from his job as a technical product manager at Amazon in March.

Dhanani has landed plenty of interviews for AI product manager roles, but he hasn't received any offers. He has worked with large language models but not since 2016; the technology has changed significantly since then. He also isn't entirely convinced that companies know what they are looking for. On top of that, two different hiring managers told him they were sifting through hundreds of applicants.

His next step is a two-week online AI boot camp from Deep Atlas costing $6,800. [1] "The skills building for me is a worthwhile investment," he said, even if it doesn't help land him a job.

The tech labor market is in an unbalanced state. There is demand for a specific type of tier-one AI talent -- namely those who have the technical knowledge or experience working with large language models, or LLMs, that fuel chatbots with the ability to generate content. There are companies seeking candidates with those skills, but not enough workers who are qualified to do them.

Then there is everyone else. Thousands of people have been laid off in the past few years, and many of those who remain employed are dealing with new management styles, reorganizations and microcuts, as more resources get shifted into AI. Those workers are now taking courses in AI, adding buzzwords to their resumes and competing in an increasingly crowded field.

Tony Phillips, co-founder of the Deep Atlas boot camp, said he has noticed a significant increase in the level of urgency that tech workers feel about the need to upskill. Deep Atlas recently added another five slots to their summer AI boot camp.

"People started to see the writing on the wall that their jobs really could be obsolete," he said. "You're probably not going to get replaced by AI. You're going to be replaced by someone who knows AI and does your job."

As of December, the number of LinkedIn members adding skills like Copilot and ChatGPT to their profiles was 142 times as great as in the year prior, according to the 2024 Work Trend Index from Microsoft and LinkedIn. The survey also found that job posts on LinkedIn that mention AI receive 17% more applications compared with non-AI roles.

One sales manager with more than a decade of experience said his software-as-a-service company has been through several reorganizations, more-intense performance management reviews and several small rounds of layoffs. He applied to jobs at OpenAI and Anthropic earlier this year but didn't get a response from either. He reckons he needed to have AI-specific experience in sales to get in the door.

Tech firms are investing heavily in AI, but they aren't going on hiring sprees as they did years ago: New tech job postings fell from an average of around 308,000 a month in 2019 to 180,000 a month as of April, according to the tech trade association CompTIA.

Jobs in AI and machine learning as a percentage of all U.S. tech job postings are growing, but they still aren't a large portion of the overall tech job market. AI and machine-learning postings grew from 9.5% of tech jobs in January 2023 to 11.5% in April 2024, according to data from labor-market analyst Lightcast. But that growth is happening amid depressed demand for tech workers overall, said Art Zeile, chief executive of DHI Group, parent company of the tech careers marketplace Dice.

Zeile said many of those hiring for AI or AI-adjacent roles are consulting firms.

"What that means is, the large companies are starting with business consultants to do pilots or prototypes," Zeile said.

Many tech workers specifically want to work at companies that are solving problems in AI, according to Nancy Xu, founder of Moonhub, a recruiting firm that hires talent for companies in artificial intelligence. They might be at a desirable tech company, she said, "but they're leaving these companies because they want to go to an AI company."

Candidates shouldn't get too discouraged about a lack of experience, Xu said. Many firms are looking for talent to build applications on top of large language models, which requires software engineering skills but not AI-specific experience.

OpenAI is willing to take a chance on workers new to the space, said Elena Chatziathanasiadou, who runs the ChatGPT-maker's residency program, which is six months long. More important than prior experience in AI, Chatziathanasiadou said, is a willingness to learn and a commitment to the company's mission. The program has accepted college dropouts, neuroscientists and a graduate from the Juilliard School who worked on an AI-based music research project.

"We care about people being able to understand the field," Chatziathanasiadou said.

Anna X. Wang, head of AI at the education-tech company Multiverse, is building a machine-learning engineering team. There are three crucial criteria: coding skills, soft skills including learning agility and the ability to cross collaborate, plus a foundational knowledge of AI theory, even if it is self-taught or acquired in online courses.

It is hard enough to find the first two, she said, but when you add the third requirement of "not just toying around with ChatGPT" but truly understanding which AI tools should be used for solving what problem, the field gets very thin.

Large tech companies are trying to make their entire workforces more AI-proficient. Trailhead, Salesforce's training platform, currently offers 43 AI-related courses ranging from fundamentals to ethical AI use. Over 60,000 Salesforce employees have taken at least one AI course.

"We believe that everyone should be reskilled and in some way have the tools they need to have to succeed in this new world," said Jayesh Govindarajan, senior vice president of Salesforce AI.

Juliet Kelso, a consultant who has worked on projects at Meta and Google identifying opportunities to use AI, pivoted into the field about a year and a half ago, before it was so competitive. She took the initiative to learn about AI herself.

"I did a research project that identified the best AI tools depending on the company size, business use case, whether the client wants to prioritize the most innovative AI offerings or platforms," she said.

Kelso has since founded Oasis Collective, a group in San Francisco that hosts networking and education events for women in AI. She said she has seen multiple founders teaching themselves how to build AI products so they can change the focus of their startups.

"There's this hierarchy of coolness within AI founders," she said. "The lowest rung of coolness would be if you have a company and you're just literally using AI tools integrated into your offering and just calling yourself an AI company."" [2]

 

1. "At Deep Atlas after completion, you'll be able to:

Explain with technical precision the training and inference processes of various ML models and algorithms, including Large Language Models like ChatGPT.

Recognize which problems can be solved using machine learning, and compare the benefits and drawbacks of simpler (shallow) versus more complex (deep) approaches.

Use Retrieval-Augmented Generation (RAG) to dynamically integrate external knowledge into Large Language Model inference.

Apply Prompt Engineering techniques to systematically guide Large Language Models for maximum performance.

Build intelligent agents on top of Large Language Models that can provide context-aware responses and take real-world action.

Apply Transfer Learning and Fine-Tuning methods to make general-purpose models work for specific tasks more effectively, using techniques like LoRA.

Contribute to end-to-end model development: from data cleaning and feature engineering to training, evaluation, and deployment.

Understand and experiment with various neural network architectures (like GANs, Transformers, RNNs, LSTMs, CNNs, and AEs) to solve different classes of problems."

2. Workers Upgrade Their Skills Amid AI Jobs Boom. Bindley, Katherine.  Wall Street Journal, Eastern edition; New York, N.Y.. 28 May 2024: A.1.